Overview

Dataset statistics

Number of variables13
Number of observations824146
Missing cells1304368
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.7 MiB
Average record size in memory104.0 B

Variable types

NUM7
CAT6

Reproduction

Analysis started2020-12-13 17:28:52.343296
Analysis finished2020-12-13 17:29:56.590065
Duration1 minute and 4.25 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

DBN has a high cardinality: 1631 distinct values High cardinality
School Name has a high cardinality: 1627 distinct values High cardinality
# Days Present is highly correlated with # Total Days and 1 other fieldsHigh correlation
# Total Days is highly correlated with # Days Present and 1 other fieldsHigh correlation
# Contributing 20+ Total Days is highly correlated with # Total Days and 1 other fieldsHigh correlation
# Chronically Absent is highly correlated with # Days AbsentHigh correlation
# Days Absent is highly correlated with # Chronically AbsentHigh correlation
Demographic Variable is highly correlated with Demographic CategoryHigh correlation
Demographic Category is highly correlated with Demographic VariableHigh correlation
# Days Absent has 215727 (26.2%) missing values Missing
# Days Present has 215727 (26.2%) missing values Missing
% Attendance has 215727 (26.2%) missing values Missing
# Contributing 20+ Total Days has 215727 (26.2%) missing values Missing
# Chronically Absent has 220730 (26.8%) missing values Missing
% Chronically Absent has 220730 (26.8%) missing values Missing
# Chronically Absent has 18887 (2.3%) zeros Zeros
% Chronically Absent has 18887 (2.3%) zeros Zeros

Variables

DBN
Categorical

HIGH CARDINALITY

Distinct count1631
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
31R080
 
1168
01M539
 
1117
75Q993
 
1086
75X188
 
1071
75K369
 
1057
Other values (1626)
818647
ValueCountFrequency (%) 
31R08011680.1%
 
01M53911170.1%
 
75Q99310860.1%
 
75X18810710.1%
 
75K36910570.1%
 
75K05310550.1%
 
75M13810480.1%
 
75Q81110440.1%
 
75M22610400.1%
 
75Q17710330.1%
 
Other values (1621)81342798.7%
 
2020-12-13T12:30:00.549910image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

School Name
Categorical

HIGH CARDINALITY

Distinct count1627
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
P.S. 212
 
1352
P.S. 253
 
1244
The Michael J. Petrides School
 
1168
New Explorations into Science, Technology and Math
 
1117
P.S. Q993
 
1086
Other values (1622)
818179
ValueCountFrequency (%) 
P.S. 21213520.2%
 
P.S. 25312440.2%
 
The Michael J. Petrides School11680.1%
 
New Explorations into Science, Technology and Math11170.1%
 
P.S. Q99310860.1%
 
P.S. X18810710.1%
 
P.S. K369 - Coy L. Cox School10570.1%
 
P.S. K05310550.1%
 
P.S. 13810480.1%
 
P.S. Q81110440.1%
 
Other values (1617)81290498.6%
 
2020-12-13T12:30:04.569951image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length50
Median length26
Mean length27.13571018
Min length5

Grade
Categorical

Distinct count15
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
All Grades
129688
0K
 
65516
1
 
65201
2
 
64559
3
 
63429
Other values (10)
435753
ValueCountFrequency (%) 
All Grades12968815.7%
 
0K655167.9%
 
1652017.9%
 
2645597.8%
 
3634297.7%
 
4621787.5%
 
5618507.5%
 
6452735.5%
 
8416255.1%
 
7416065.0%
 
Other values (5)18322122.2%
 
2020-12-13T12:30:08.659496image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length18
Median length1
Mean length3.305443453
Min length1

Year
Categorical

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
2015-16
138124
2016-17
138093
2017-18
137873
2018-19
137532
2014-15
137033
ValueCountFrequency (%) 
2015-1613812416.8%
 
2016-1713809316.8%
 
2017-1813787316.7%
 
2018-1913753216.7%
 
2014-1513703316.6%
 
2013-1413549116.4%
 
2020-12-13T12:30:12.803975image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Demographic Category
Categorical

HIGH CORRELATION

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
Ethnicity
274610
Gender
126575
Poverty
125940
SWD Status
116918
ELL Status
115891
ValueCountFrequency (%) 
Ethnicity27461033.3%
 
Gender12657515.4%
 
Poverty12594015.3%
 
SWD Status11691814.2%
 
ELL Status11589114.1%
 
All Students642127.8%
 
2020-12-13T12:30:17.135052image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length12
Median length9
Mean length8.749850148
Min length6

Demographic Variable
Categorical

HIGH CORRELATION

Distinct count14
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
All Students
 
64212
Poverty
 
63998
Male
 
63353
Hispanic
 
63311
Female
 
63222
Other values (9)
506050
ValueCountFrequency (%) 
All Students642127.8%
 
Poverty639987.8%
 
Male633537.7%
 
Hispanic633117.7%
 
Female632227.7%
 
Not Poverty619427.5%
 
Black607557.4%
 
Not ELL601747.3%
 
SWD595247.2%
 
Not SWD573947.0%
 
Other values (4)20626125.0%
 
2020-12-13T12:30:21.209166image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length12
Median length6
Mean length6.387603653
Min length3

# Total Days
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count86622
Unique (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14454.985155542829
Minimum1
Maximum976375
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2020-12-13T12:30:21.409412image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile178
Q11849
median6224
Q314270
95-th percentile60832.75
Maximum976375
Range976374
Interquartile range (IQR)12421

Descriptive statistics

Standard deviation29094.9427
Coefficient of variation (CV)2.012796442
Kurtosis96.75291348
Mean14454.98516
Median Absolute Deviation (MAD)5173
Skewness7.175055511
Sum1.19130182e+10
Variance846515690.5
2020-12-13T12:30:21.562946image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
178108021.3%
 
35666910.8%
 
17644610.5%
 
53443360.5%
 
18234230.4%
 
18032110.4%
 
71229630.4%
 
35225730.3%
 
89022280.3%
 
36419870.2%
 
Other values (86612)78147194.8%
 
ValueCountFrequency (%) 
113210.2%
 
210850.1%
 
37270.1%
 
45610.1%
 
55580.1%
 
ValueCountFrequency (%) 
9763751< 0.1%
 
9762101< 0.1%
 
9637681< 0.1%
 
9542272< 0.1%
 
9429341< 0.1%
 

# Days Absent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct count16802
Unique (%)2.8%
Missing215727
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean1491.2436002162983
Minimum0.0
Maximum105055.0
Zeros7
Zeros (%)< 0.1%
Memory size6.3 MiB
2020-12-13T12:30:21.724558image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile96
Q1298
median646
Q31423
95-th percentile6003
Maximum105055
Range105055
Interquartile range (IQR)1125

Descriptive statistics

Standard deviation2864.866744
Coefficient of variation (CV)1.921125927
Kurtosis118.2651455
Mean1491.2436
Median Absolute Deviation (MAD)430
Skewness7.952174774
Sum907300940
Variance8207461.462
2020-12-13T12:30:21.868299image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1586870.1%
 
1266830.1%
 
1156670.1%
 
1216650.1%
 
1416610.1%
 
1856590.1%
 
1746560.1%
 
1336550.1%
 
1566540.1%
 
2016530.1%
 
Other values (16792)60177973.0%
 
(Missing)21572726.2%
 
ValueCountFrequency (%) 
07< 0.1%
 
21< 0.1%
 
35< 0.1%
 
47< 0.1%
 
57< 0.1%
 
ValueCountFrequency (%) 
1050551< 0.1%
 
1010871< 0.1%
 
869031< 0.1%
 
868951< 0.1%
 
865131< 0.1%
 

# Days Present
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct count81134
Unique (%)13.3%
Missing215727
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean16858.884162065944
Minimum8.0
Maximum934266.0
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2020-12-13T12:30:22.053106image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile1350
Q13670
median8009
Q316020
95-th percentile66871.2
Maximum934266
Range934258
Interquartile range (IQR)12350

Descriptive statistics

Standard deviation30011.05309
Coefficient of variation (CV)1.780132825
Kurtosis76.52416301
Mean16858.88416
Median Absolute Deviation (MAD)5145
Skewness6.436099518
Sum1.025726544e+10
Variance900663307.4
2020-12-13T12:30:22.190189image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
102092< 0.1%
 
99789< 0.1%
 
152587< 0.1%
 
103286< 0.1%
 
120985< 0.1%
 
103984< 0.1%
 
119682< 0.1%
 
119882< 0.1%
 
118482< 0.1%
 
101382< 0.1%
 
Other values (81124)60756873.7%
 
(Missing)21572726.2%
 
ValueCountFrequency (%) 
81< 0.1%
 
211< 0.1%
 
951< 0.1%
 
1321< 0.1%
 
1591< 0.1%
 
ValueCountFrequency (%) 
9342661< 0.1%
 
9225431< 0.1%
 
9150102< 0.1%
 
9047501< 0.1%
 
8869671< 0.1%
 

% Attendance
Real number (ℝ≥0)

MISSING

Distinct count665
Unique (%)0.1%
Missing215727
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean91.29663751460751
Minimum0.7
Maximum100.0
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2020-12-13T12:30:22.348167image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile81.1
Q189.4
median92.6
Q394.7
95-th percentile96.9
Maximum100
Range99.3
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation5.333850844
Coefficient of variation (CV)0.05842330002
Kurtosis10.98365427
Mean91.29663751
Median Absolute Deviation (MAD)2.5
Skewness-2.352753332
Sum55546608.9
Variance28.44996483
2020-12-13T12:30:22.485642image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
94.176020.9%
 
94.374160.9%
 
93.974110.9%
 
94.573940.9%
 
9473890.9%
 
93.873860.9%
 
94.673500.9%
 
94.273280.9%
 
93.673030.9%
 
94.472820.9%
 
Other values (655)53455864.9%
 
(Missing)21572726.2%
 
ValueCountFrequency (%) 
0.71< 0.1%
 
1.71< 0.1%
 
9.91< 0.1%
 
121< 0.1%
 
12.11< 0.1%
 
ValueCountFrequency (%) 
1008< 0.1%
 
99.93< 0.1%
 
99.89< 0.1%
 
99.715< 0.1%
 
99.621< 0.1%
 

# Contributing 20+ Total Days
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct count2440
Unique (%)0.4%
Missing215727
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean110.23824371033778
Minimum5.0
Maximum5940.0
Zeros0
Zeros (%)0.0%
Memory size6.3 MiB
2020-12-13T12:30:22.636228image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9
Q124
median53
Q3105
95-th percentile439
Maximum5940
Range5935
Interquartile range (IQR)81

Descriptive statistics

Standard deviation195.7382183
Coefficient of variation (CV)1.775592678
Kurtosis82.52221113
Mean110.2382437
Median Absolute Deviation (MAD)34
Skewness6.678971413
Sum67071042
Variance38313.45012
2020-12-13T12:30:22.785494image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
886831.1%
 
1186771.1%
 
986401.0%
 
1085541.0%
 
1384851.0%
 
784541.0%
 
1283961.0%
 
1481981.0%
 
1881571.0%
 
1680851.0%
 
Other values (2430)52409063.6%
 
(Missing)21572726.2%
 
ValueCountFrequency (%) 
57500.1%
 
672590.9%
 
784541.0%
 
886831.1%
 
986401.0%
 
ValueCountFrequency (%) 
59401< 0.1%
 
58631< 0.1%
 
58462< 0.1%
 
57741< 0.1%
 
56681< 0.1%
 

# Chronically Absent
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct count934
Unique (%)0.2%
Missing220730
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean27.967415514338366
Minimum0.0
Maximum1596.0
Zeros18887
Zeros (%)2.3%
Memory size6.3 MiB
2020-12-13T12:30:22.953798image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q328
95-th percentile114
Maximum1596
Range1596
Interquartile range (IQR)23

Descriptive statistics

Standard deviation52.3267896
Coefficient of variation (CV)1.870991246
Kurtosis84.97170772
Mean27.96741551
Median Absolute Deviation (MAD)9
Skewness6.748101366
Sum16875986
Variance2738.09291
2020-12-13T12:30:23.118556image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3314373.8%
 
2308323.7%
 
4294343.6%
 
1279143.4%
 
5277163.4%
 
6256993.1%
 
7238512.9%
 
8218202.6%
 
9202842.5%
 
0188872.3%
 
Other values (924)34554241.9%
 
(Missing)22073026.8%
 
ValueCountFrequency (%) 
0188872.3%
 
1279143.4%
 
2308323.7%
 
3314373.8%
 
4294343.6%
 
ValueCountFrequency (%) 
15961< 0.1%
 
15861< 0.1%
 
15761< 0.1%
 
14651< 0.1%
 
14581< 0.1%
 

% Chronically Absent
Real number (ℝ≥0)

MISSING
ZEROS

Distinct count984
Unique (%)0.2%
Missing220730
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean27.720951880626302
Minimum0.0
Maximum100.0
Zeros18887
Zeros (%)2.3%
Memory size6.3 MiB
2020-12-13T12:30:23.300712image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.1
Q113.5
median25
Q339.4
95-th percentile61.3
Maximum100
Range100
Interquartile range (IQR)25.9

Descriptive statistics

Standard deviation18.06207498
Coefficient of variation (CV)0.6515676324
Kurtosis0.04975180387
Mean27.72095188
Median Absolute Deviation (MAD)12.5
Skewness0.6709151033
Sum16727265.9
Variance326.2385527
2020-12-13T12:30:23.449110image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0188872.3%
 
33.3106461.3%
 
5095341.2%
 
2584141.0%
 
16.772470.9%
 
2066480.8%
 
14.362220.8%
 
28.654540.7%
 
12.552110.6%
 
4046700.6%
 
Other values (974)52048363.2%
 
(Missing)22073026.8%
 
ValueCountFrequency (%) 
0188872.3%
 
0.21< 0.1%
 
0.311< 0.1%
 
0.441< 0.1%
 
0.544< 0.1%
 
ValueCountFrequency (%) 
100336< 0.1%
 
991< 0.1%
 
98.91< 0.1%
 
98.61< 0.1%
 
98.52< 0.1%
 

Interactions

2020-12-13T12:29:24.408581image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:24.893512image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:25.357393image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:25.821908image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:26.312665image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:26.799245image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:27.319573image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:27.831737image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:28.305589image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:28.753788image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:29.228538image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:29.679864image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:30.143861image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:30.624697image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:31.235908image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:31.684046image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:32.139360image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:32.618793image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:33.118740image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:33.589286image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:34.063878image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:34.547473image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:35.006658image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:35.471174image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:35.934567image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:36.386252image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:36.848356image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:37.334325image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:37.822138image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:38.338218image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:38.808196image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:39.307560image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:39.763907image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:40.256367image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:40.819842image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:41.301580image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:41.769303image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:42.250939image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:42.742528image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:43.232338image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:43.739250image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:44.255538image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:45.775400image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:46.258400image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:46.720354image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:47.189405image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:47.750914image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:48.210169image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:48.678253image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-12-13T12:30:23.586320image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-13T12:30:23.828710image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-13T12:30:24.080047image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-13T12:30:24.313706image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-13T12:30:24.561218image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-13T12:29:50.298200image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:51.798707image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:54.811961image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-12-13T12:29:55.420535image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

DBNSchool NameGradeYearDemographic CategoryDemographic Variable# Total Days# Days Absent# Days Present% Attendance# Contributing 20+ Total Days# Chronically Absent% Chronically Absent
001M015P.S. 015 Roberto ClementeAll Grades2013-14All StudentsAll Students348032783.032020.092.0216.058.026.9
101M015P.S. 015 Roberto ClementeAll Grades2014-15All StudentsAll Students334552374.031081.092.9197.046.023.4
201M015P.S. 015 Roberto ClementeAll Grades2015-16All StudentsAll Students298402071.027769.093.1186.051.027.4
301M015P.S. 015 Roberto ClementeAll Grades2016-17All StudentsAll Students306011994.028607.093.5193.048.024.9
401M015P.S. 015 Roberto ClementeAll Grades2017-18All StudentsAll Students332642078.031186.093.8195.037.019.0
501M015P.S. 015 Roberto ClementeAll Grades2018-19All StudentsAll Students308872278.028609.092.6186.045.024.2
601M015P.S. 015 Roberto ClementePK in K-12 Schools2013-14All StudentsAll Students4711560.04151.088.130.016.053.3
701M015P.S. 015 Roberto ClementePK in K-12 Schools2014-15All StudentsAll Students3395484.02911.085.723.015.065.2
801M015P.S. 015 Roberto ClementePK in K-12 Schools2015-16All StudentsAll Students2193248.01945.088.718.09.050.0
901M015P.S. 015 Roberto ClementePK in K-12 Schools2016-17All StudentsAll Students2844NaNNaNNaNNaNNaNNaN

Last rows

DBNSchool NameGradeYearDemographic CategoryDemographic Variable# Total Days# Days Absent# Days Present% Attendance# Contributing 20+ Total Days# Chronically Absent% Chronically Absent
82413675X811P.S. X811122014-15ELL StatusELL118371953.09884.083.567.040.059.7
82413775X811P.S. X811122014-15ELL StatusNot ELL374726218.031254.083.4213.0122.057.3
82413875X811P.S. X811122015-16ELL StatusELL163322606.013726.084.096.056.058.3
82413975X811P.S. X811122015-16ELL StatusNot ELL376395673.031966.084.9222.0115.051.8
82414075X811P.S. X811122016-17ELL StatusELL224363809.018627.083.0132.085.064.4
82414175X811P.S. X811122016-17ELL StatusNot ELL325585543.027015.083.0192.098.051.0
82414275X811P.S. X811122017-18ELL StatusELL228184079.018739.082.1133.081.060.9
82414375X811P.S. X811122017-18ELL StatusNot ELL345425791.028751.083.2201.0112.055.7
82414475X811P.S. X811122018-19ELL StatusELL249104837.020073.080.6147.098.066.7
82414575X811P.S. X811122018-19ELL StatusNot ELL374885849.031639.084.4215.0102.047.4